Portrait of Dhanya Sridhar

Dhanya Sridhar

Core Academic Member
dhanya.sridhar@mila.quebec
Canada CIFAR AI Chair
Assistant Professor, Université de Montréal, Department of Computer Science and Operations Research
Research Topics
Causality
Deep Learning
Probabilistic Models
Reasoning
Representation Learning
Website
Google Scholar

Biography

Dhanya Sridhar is an assistant professor in the Department of Computer Science and Operations Research (DIRO) at Université de Montréal, a core academic member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair.

She was a postdoctoral researcher at Columbia University and received her doctorate from the University of California, Santa Cruz.

In brief, Sridhar’s research focuses on combining causality and machine learning in service of AI systems that are robust to distribution shifts, adapt to new tasks efficiently and discover new knowledge alongside us.

Current Students

Philippe Brouillard
PhD - Université de Montréal
Co-supervisor :
Alexandre Drouin
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Navita Goyal
Independent visiting researcher - University of Maryland College Park
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Sophia Gunluk
PhD - Université de Montréal
Website
Shruti Joshi
PhD - Université de Montréal
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Tejas Kasetty
Master's Research - Université de Montréal
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Cristian Dragos Manta
PhD - Université de Montréal
Principal supervisor :
Yoshua Bengio
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Tom Marty
PhD - Université de Montréal
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Mizu Nishikawa-Toomey
PhD - Université de Montréal
Principal supervisor :
Laurent Charlin
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Theo Saulus
PhD - Université de Montréal
Website

Publications

Causal inference from text: A commentary
Dhanya Sridhar
David Blei
2022-10-18
Science Advances (published)
doi.org
Estimating Social Influence from Observational Data
Dhanya Sridhar
Caterina De Bacco
David Blei
We consider the problem of estimating social influence, the effect that a person's behavior has on the future behavior of their peers. The k… (see more)ey challenge is that shared behavior between friends could be equally explained by influence or by two other confounding factors: 1) latent traits that caused people to both become friends and engage in the behavior, and 2) latent preferences for the behavior. This paper addresses the challenges of estimating social influence with three contributions. First, we formalize social influence as a causal effect, one which requires inferences about hypothetical interventions. Second, we develop Poisson Influence Factorization (PIF), a method for estimating social influence from observational data. PIF fits probabilistic factor models to networks and behavior data to infer variables that serve as substitutes for the confounding latent traits. Third, we develop assumptions under which PIF recovers estimates of social influence. We empirically study PIF with semi-synthetic and real data from Last.fm, and conduct a sensitivity analysis. We find that PIF estimates social influence most accurately compared to related methods and remains robust under some violations of its assumptions.
2022-06-27
Proceedings of the First Conference on Causal Learning and Reasoning (published)
doi.org
proceedings.mlr.press
Heterogeneous Supervised Topic Models
Dhanya Sridhar
Hal Daumé III
David Blei
Researchers in the social sciences are often interested in the relationship between text and an outcome of interest, where the goal is to bo… (see more)th uncover latent patterns in the text and predict outcomes for unseen texts. To this end, this paper develops the heterogeneous supervised topic model (HSTM), a probabilistic approach to text analysis and prediction. HSTMs posit a joint model of text and outcomes to find heterogeneous patterns that help with both text analysis and prediction. The main benefit of HSTMs is that they capture heterogeneity in the relationship between text and the outcome across latent topics. To fit HSTMs, we develop a variational inference algorithm based on the auto-encoding variational Bayes framework. We study the performance of HSTMs on eight datasets and find that they consistently outperform related methods, including fine-tuned black-box models. Finally, we apply HSTMs to analyze news articles labeled with pro- or anti-tone. We find evidence of differing language used to signal a pro- and anti-tone.
2021-12-31
Transactions of the Association for Computational Linguistics (published)
doi.org